Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples
| Autor: | Jinsong Xie, Yuanhong Chang, Zitong Zhou, Tongyang Pan, Jinglong Chen |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Discriminator Computer science 02 engineering and technology Fault (power engineering) Convolutional neural network law.invention 020901 industrial engineering & automation law 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation Bearing (mechanical) Artificial neural network business.industry Applied Mathematics Deep learning 020208 electrical & electronic engineering Pattern recognition Automation Computer Science Applications Identification (information) ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering Artificial intelligence business |
| Zdroj: | ISA Transactions. 101:379-389 |
| ISSN: | 0019-0578 |
| Popis: | Rolling bearings are the widely used parts in most of the industrial automation systems. As a result, intelligent fault identification of rolling bearing is important to ensure the stable operation of the industrial automation systems. However, a major problem in intelligent fault identification is that it needs a large number of labeled samples to obtain a well-trained model. Aiming at this problem, the paper proposes a semi-supervised multi-scale convolutional generative adversarial network for bearing fault identification which uses partially labeled samples and sufficient unlabeled samples for training. The network adopts a one-dimensional multi-scale convolutional neural network as the discriminator and a multi-scale deconvolutional neural network as the generator and the model is trained through an adversarial process. Because of the full use of unlabeled samples, the proposed semi-supervised model can detect the faults in bearings with limited labeled samples. The proposed method is tested on three datasets and the average classification accuracy arrived at of 100%, 99.28% and 96.58% respectively Results indicate that the proposed semi-supervised convolutional generative adversarial network achieves satisfactory performance in bearing fault identification when the labeled data are insufficient. |
| Databáze: | OpenAIRE |
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